What you get
- LLM features running in production with predictable cost and quality
- Provider-agnostic architecture that keeps you free of vendor lock-in
- Your team able to manage prompts, monitor quality, and swap models without us
Build
LLM integration services
LLM features inside your product, production-ready in 12 weeks.
30+
LLM integrations
40%
Average cost cut
12
Weeks to launch
Trusted by teams at
The Problem
What problem does this service solve?
Your product roadmap has LLM features on it, but your team hasn't built prompt engineering, model evaluation, cost optimization, or the provider abstraction a production LLM workload needs.
Every month you wait, users see LLM features launch in competing products. The expectation bar rises and your window to lead narrows.
Overview
What is LLM integration services?
Adding an LLM API call is the easy part. Making it reliable, cost-efficient, and something your team can actually maintain - that's where most integrations fall apart. We build the version that holds up.
Adding an LLM API call takes a day. Shipping a reliable LLM feature users trust takes structured engineering around prompts, evaluation, cost, and failure handling. That's the gap we close.
We build LLM integrations as maintainable product components with model abstraction, prompt versioning, and observability - so your team isn't locked to one provider or one prompt.
You get LLM capabilities inside your product that work reliably at scale, not a fragile prototype that breaks the next time the API changes.
Experience Signal
Integrated LLMs into 30+ production products across SaaS, healthcare, fintech, and marketplace platforms. 12 weeks is our default, not our stretch goal.
What we build
LLM integration services services we deliver
Summarization and extraction features
Document summaries, structured extraction, and key-point distillation wired into your product with source citations and accuracy tracking.
Classification and moderation
LLM-based classification with structured output schemas, confidence scoring, and human review queues for low-confidence predictions.
Multi-provider abstraction layers
Provider-agnostic architecture with automatic fallback routing, cost-based model selection, and unified logging across every model you use.
Prompt libraries and versioning
Prompt management with versioning, A/B testing, and evaluation so prompt changes roll out safely instead of breaking production.
Cost optimization and caching
Token budgets, response caching, model tiering, and prompt compression that cut LLM spend 20-40% without losing output quality.
Structured output and tool use
JSON schema validation, function calling, and tool use wiring so LLM output slots into your existing systems cleanly.
Self-hosted LLM deployment
Llama, Mistral, and Qwen deployed on vLLM or cloud GPU infrastructure for privacy, latency, or cost-driven workloads.
LLM observability and evaluation
Live dashboards for cost, latency, error rate, and output quality - plus eval harnesses that catch regressions before users do.
Fit
Is this service right for you?
Good fit
- SaaS teams adding summarization, classification, extraction, or generation features
- Products that need multiple LLM providers with automatic fallback routing
- Engineering teams shipping their first LLM-powered features
- Orgs migrating from one LLM provider to another without downtime
- Teams that need cost discipline on a fast-growing LLM bill
Not the right fit
- Teams looking for a simple API wrapper with no product integration
- Projects where the LLM use case hasn't been validated with real users
- Orgs that want to train foundation models from scratch
Process
How does LLM integration services delivery work?
1
Phase 1· Week 1-2
Use case scoping and model evaluation
We define the LLM use cases inside your product, benchmark candidate models, and pick the best model for each task on quality, speed, and cost.
Deliverables
- LLM use case specs with acceptance criteria
- Model benchmark results - quality, latency, cost
- Model strategy with fallback options
2
Phase 2· Week 2-4
Integration architecture and prompt engineering
We design the integration architecture with provider abstraction, build the prompt library, and wire up the evaluation framework before production code lands.
Deliverables
- LLM integration architecture with provider abstraction layer
- Prompt library with versioning and testing framework
- Evaluation suite with quality benchmarks
3
Phase 3· Week 4-9
Build, instrument, and optimize
We build the LLM features inside your product, instrument cost and quality tracking, and tune prompts against real usage patterns every week.
Deliverables
- Production LLM features with full product integration
- Cost and quality monitoring dashboard
- Token usage optimization and caching strategy
4
Phase 4· Week 9-12
Production hardening and handover
We finalize reliability controls, document the integration, and hand the prompts, models, and cost levers over to your team with a runbook.
Deliverables
- Production deployment with rate limiting and circuit breakers
- Integration documentation and architecture guide
- Team enablement for prompt management and model updates
Outcomes
- LLM features running in production with predictable cost and quality
- Provider-agnostic architecture that keeps you free of vendor lock-in
- Your team able to manage prompts, monitor quality, and swap models without us
- A cost structure that holds up as LLM usage grows
Deliverables
- Model evaluation report with benchmark data
- Provider abstraction layer with multi-model support
- Prompt library with versioning and A/B testing
- Production LLM features integrated into your product
- Cost and quality monitoring dashboard
- Integration documentation and team enablement guide
Success Metrics
- LLM feature adoption and task completion rate
- Average cost per LLM call by feature type
- Response quality score against the evaluation rubric
- API reliability - uptime and error rate
- Token usage trend over time against budget
Engagement models
12-week end-to-end delivery for LLM integration into an existing product with abstraction, monitoring, and cost controls.
Best forTeams adding their first LLM features to a shipping product.
AI models we work with
GPT-5
OpenAI
General-purpose LLM features - summarization, extraction, classification, generation with strong instruction following.
Claude Sonnet 4.6
Anthropic
High-volume production LLM workloads where cost per call has to stay predictable.
Claude Opus 4.6
Anthropic
Long-context features that read contracts, policies, or full document sets before answering.
Gemini 2.5 Pro
Multi-modal LLM features that combine text with images, PDFs, or structured data.
Llama 3.3
Meta
Self-hosted LLM workloads for privacy, latency, or cost reasons - data never leaves your perimeter.
Mistral Large
Mistral
European data residency and cost-efficient self-hosted deployments with strong multilingual output.
Use Cases
Common use cases for LLM integration services
Smart summarization for document management
Users upload lengthy documents and need quick, accurate summaries with key point extraction.
How we build it
We integrate an LLM pipeline that chunks documents, generates hierarchical summaries, and extracts structured metadata with citation links back to source paragraphs.
Outcome
80% drop in document review time with 95%+ summary accuracy against human baselines.
Multi-provider LLM gateway for enterprise
An enterprise platform needs LLM features but can't depend on a single provider because of compliance and availability requirements.
How we build it
We build a provider abstraction layer with automatic fallback routing, cost-based model selection, and unified logging across OpenAI, Anthropic, and self-hosted models.
Outcome
99.9% LLM feature availability with 30% cost cut through smart routing.
AI classification engine for a marketplace
A marketplace gets thousands of listings daily that need consistent categorization and content moderation.
How we build it
We integrate LLM-based classification with structured output schemas, confidence scoring, and human review queues for low-confidence predictions.
Outcome
90% of listings auto-classified correctly. Human review focused only on edge cases.
What clients say
Real feedback from real teams
I spent years at Amazon fighting static surveys. RaftLabs built a working prototype in four days that already outperformed every survey tool I'd used. Twelve weeks later we had a full SaaS that product teams actually want to use.
Founder
Ex-Amazon PM - Perceptional
We went from text surveys that nobody finished to AI phone interviews that people actually enjoy. The voice agents handle the whole conversation, and the analytics tell us what we need to know without reading a single transcript.
Cherian Koshy
Behavioral Strategist - USA Today Bestselling Author
Proof
Recent llm integration services work
BuildLLM-powered survey SaaS for Perceptional
4x
Deeper insights
12 weeks
Concept to launch
“Working prototype in four days. Full SaaS in twelve weeks.”
Read case studyBuildLLM reasoning layer for Cherian Koshy
12 weeks
To production
Global
Call reach
“Text surveys nobody finished became phone interviews people enjoy.”
Read case studyIndustries
LLM integration services for your industry
SaaS
In-product LLM features - summarization, extraction, classification - that cut ticket volume and make users faster.
ExploreRetail
Product description generation, search, and classification across huge catalogs with cost controls that hold at scale.
ExploreFintech
Document extraction, compliance summarization, and KYC helpers with audit trails and provider abstraction.
ExploreHealthcare
Clinical document summarization and structured extraction with HIPAA-conscious self-hosted options.
ExploreInsurance
Claims summarization, policy extraction, and underwriting helpers that cut handle time without cutting accuracy.
ExploreEdTech
Lesson generation, feedback scoring, and personalized explanation features grounded in your curriculum.
ExploreFrequently asked questions about LLM integration services
We integrate OpenAI (GPT-5, GPT-4o), Anthropic (Claude Opus and Sonnet 4.6), Google (Gemini 2.5 Pro), and open-source models like Llama 3.3 and Mistral. Model choice follows your use case - we benchmark before we pick.
Related Services
Next Step
Know which LLM feature you want but not how to ship it?
Describe the capability. We'll scope the integration, estimate cost, and show you the fastest path to a production rollout your team can maintain.